This paper introduces a novel transformer-based deep model that explicitly embeds global context and calculates spatio-temporal global-local correlation for egocentric gaze estimation. Specifically, we design a transformer encoder that adopts a global visual token embedding strategy to incorporate the global scene context. We then introduce a novel Global-Local Correlation (GLC) module that highlights the connection between global and local visual tokens. Finally, we adopt a transformer-based decoder to produce gaze prediction output. We evaluate our approach on two egocentric video datasets – EGTEA Gaze+ [1]} and Ego4D [2]}. Our proposed model is easy to incorporate into existing transformer-based video analysis architectures, and we show that it yields an improvement of more than \(3.9\%\)  in F1 score over SOTA methods for egocentric gaze estimation. The code and pretrained models will be made publicly available to the research community. In summary, this work makes the following contributions:
